Interpolation routines based on polynomials, splines, linear triangulation, and distance weighting techniques are tested. Two data sets containing irregularly distributed point values with two independent variables (wavelength and angle of incidence) are used as input data. The accuracy of interpolated values at unvisited points and processing time are used as criteria to determine the merits of the various interpolation algorithms. Effectiveness of distance weighting methods was found to be largely dependent on the number of neighbors used. For both gradually and abruptly changing data, the most accurate models used squared inverse distance weighting. Linear triangulation was found to be the fastest method.
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